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| Group sparse representation-based classification method of bearing faults based on index redundant dictionary |
| DENG Tao1,LIN Jianhui2,HUANG Chenguang2,JIN Hang2 |
1. College of Electrical & Information Engineering,Southwest Minzu University,Chengdu 610041,China;
2. State Key Laboratory of Traction Power，Southwest Jiaotong University,Chengdu 610031,China |
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Abstract To diagnose and classify early fault states of high-speed train wheel pairs’ bearings based on acoustic emission signals are very complicated,and to set parameters and cope with multi-classification problems are difficult with commonly used artificial neural network and SVM. The group sparse representation-based classification (GSRC) method can be used to realize ideal multi-classification through sparse reconstruction under a super-complete dictionary,and it becomes a hot spot in image and speech classification. Here,a composite bearing faults redundant dictionary with indexes was designed for the GSRC method to be used in bearing fault diagnosis. Small volume advantage pre-allocation of index dictionary constructed with multi-scale permutation entropy of sample signals was used to narrow the range of fault classification. The neighborhood gradient method and the optimal first order accelerated least absolute shrinkage and selection operator (LASSO) constrained optimization algorithm were used to improve convergence and computation speed. The improved EEMD method combined with the variational mode decomposition (VMD) was used to adaptively obtain initial atoms of various fault classes,and keep faults’ nonlinear features. An interval translation sparse coding (ITSC) method was proposed to relax requirements of sample data interception to make atoms have better compactness and sparseness. Classification of running acoustic emission signals was conducted for 7 kinds of bearing defect test bench to verify the effectiveness of the proposed method.
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Received: 10 November 2017
Published: 28 March 2019
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